Continuous Indexing of Fibrosis (CIF): improving the assessment and classification of MPN patients

Development and validation of a deep learning model for morphological assessment of myeloproliferative neoplasms using clinical data and digital pathology

The subjectivity of morphological assessment and the overlapping pathological features of different subtypes of myeloproliferative neoplasms (MPNs) make accurate diagnosis challenging. To improve the pathological assessment of MPNs, we developed a diagnosis model (fusion model) based on the combination of bone marrow whole-slide images (deep learning [DL] model) and clinical parameters (clinical model). Thousand and fifty-one MPN and non-MPN patients were divided into the training, internal testing and one internal and two external validation cohorts (the combined validation cohort). In the combined validation cohort, fusion model achieved higher areas under curve (AUCs) than clinical or DL model or both for MPNs and subtype identification. Compared with haematopathologists with different experience, clinical model achieved AUC which was comparable to seniors and higher than juniors (p = 0.0208) for polycythaemia vera. The AUCs of fusion model were comparable to seniors and higher than juniors for essential thrombocytosis (p = 0.0141), prefibrotic primary myelofibrosis (p = 0.0085) and overt primary myelofibrosis (p = 0.0330) identification. In conclusion, the performances of our proposed models are equivalent to senior haematopathologists and better than juniors, providing a new perspective on the utilization of DL algorithms in MPN morphological assessment.

Application of Pathomic Features for Differentiating Dysplastic Cells in Patients with Myelodysplastic Syndrome

Myelodysplastic syndromes (MDSs) are a group of hematologic neoplasms accompanied by dysplasia of bone marrow (BM) hematopoietic cells with cytopenia. Recently, digitalized pathology and pathomics using computerized feature analysis have been actively researched for classifying and predicting prognosis in various tumors of hematopoietic tissues. This study analyzed the pathomic features of hematopoietic cells in BM aspiration smears of patients with MDS according to each hematopoietic cell lineage and dysplasia. We included 24 patients with an MDS and 21 with normal BM. The 12,360 hematopoietic cells utilized were to be classified into seven types: normal erythrocytes, normal granulocytes, normal megakaryocytes, dysplastic erythrocytes, dysplastic granulocytes, dysplastic megakaryocytes, and others. Four hundred seventy-six pathomic features quantifying cell intensity, shape, and texture were extracted from each segmented cell. After comparing the combination of feature selection and machine learning classifier methods using 5-fold cross-validation area under the receiver operating characteristic curve (AUROC), the quadratic discriminant analysis (QDA) with gradient boosting decision tree (AUROC = 0.63) and QDA with eXtreme gradient boosting (XGB) (AUROC = 0.64) showed a high AUROC combination. Through a feature selection process, 30 characteristics were further analyzed. Dysplastic erythrocytes and granulocytes showed lower median values on heatmap analysis compared to that of normal erythrocytes and granulocytes. The data suggest that pathomic features could be applied to cell differentiation in hematologic malignancies. It could be used as a new biomarker with an auxiliary role for more accurate diagnosis. Further studies including prediction survival and prognosis with larger cohort of patients are needed.

Wong Z, Karali CS, Sirinukunwattana K, Ryou H, Norfo R, Cheng Q, Carrelha J, Ren Z, Thongjuea S, Rathinam VA, Krishnan A, Royston D, Rabinovich GA, Mead AJ, Psaila B. A proinflammatory stem cell niche drives myelofibrosis through a targetable galectin-1 axis

Myeloproliferative neoplasms are stem cell-driven cancers associated with a large burden of morbidity and mortality. Most patients present with early-stage disease, but a substantial proportion progress to myelofibrosis or secondary leukemia, advanced cancers with a poor prognosis and high symptom burden. Currently, it remains difficult to predict progression, and therapies that reliably prevent or reverse fibrosis are lacking. A major bottleneck to the discovery of disease-modifying therapies has been an incomplete understanding of the interplay between perturbed cellular and molecular states. Several cell types have individually been implicated, but a comprehensive analysis of myelofibrotic bone marrow is lacking. We therefore mapped the cross-talk between bone marrow cell types in myelofibrotic bone marrow. We found that inflammation and fibrosis are orchestrated by a "quartet" of immune and stromal cell lineages, with basophils and mast cells creating a TNF signaling hub, communicating with megakaryocytes, mesenchymal stromal cells, and proinflammatory fibroblasts. We identified the β-galactoside-binding protein galectin-1 as a biomarker of progression to myelofibrosis and poor survival in multiple patient cohorts and as a promising therapeutic target, with reduced myeloproliferation and fibrosis in vitro and in vivo and improved survival after galectin-1 inhibition. In human bone marrow organoids, TNF increased galectin-1 expression, suggesting a feedback loop wherein the proinflammatory myeloproliferative neoplasm clone creates a self-reinforcing niche, fueling progression to advanced disease. This study provides a resource for studying hematopoietic cell-niche interactions, with relevance for cancer-associated inflammation and disorders of tissue fibrosis.

Applications of artificial intelligence to myeloproliferative neoplasms: a narrative review

INTRODUCTION

Artificial intelligence (AI) is a rapidly growing field of computational research with the potential to extract nuanced biomarkers for the prediction of outcomes of interest. AI implementations for the prediction for clinical outcomes for myeloproliferative neoplasms (MPNs) are currently under investigation.

AREAS COVERED

In this narrative review, we discuss AI investigations for the improvement of MPN clinical care utilizing either clinically available data or experimental laboratory findings. Abstracts and manuscripts were identified upon querying PubMed and the American Society of Hematology conference between 2000 and 2023. Overall, multidisciplinary researchers have developed AI methods in MPNs attempting to improve diagnostic accuracy, risk prediction, therapy selection, or pre-clinical investigations to identify candidate molecules as novel therapeutic agents.

EXPERT OPINION

It is our expert opinion that AI methods in MPN care and hematology will continue to grow with increasing clinical utility. We believe that AI models will assist healthcare workers as clinical decision support tools if appropriately developed with AI-specific regulatory guidelines. Though the reported findings in this review are early investigations for AI in MPNs, the collective work developed by the research community provides a promising framework for improving decision-making in the future of MPN clinical care.

Genetic alterations in myeloproliferative and myelodysplastic/myeloproliferative neoplasms - a practical guide to WHO-HAEM5

Within the World Health Organization (WHO) classification of haematopoietic neoplasms, particularly its fifth version from 2022 (WHO-HAEM5), myeloid neoplasms are not only grouped into myeloproliferative (MPN) and myelodysplastic neoplasms (MDS). There is also a group of haematological disorders that share features of both categories termed myelodysplastic /myeloproliferative neoplasms (MDS/MPN). In this article, we aim to provide a comprehensive and practical guide to WHO-HAEM5 highlighting the genetic alterations that underlie MPN and MDS/MPN. This guide provides an overview of the overlapping commonalities among these entities, as well as their unique characteristics.

Revisiting Circulating Extracellular Matrix Fragments as Disease Markers in Myelofibrosis and Related Neoplasms

Philadelphia chromosome-negative chronic myeloproliferative neoplasms (MPNs) arise due to acquired somatic driver mutations in stem cells and develop over 10-30 years from the earliest cancer stages (essential thrombocythemia, polycythemia vera) towards the advanced myelofibrosis stage with bone marrow failure. The JAK2V617F mutation is the most prevalent driver mutation. Chronic inflammation is considered to be a major pathogenetic player, both as a trigger of MPN development and as a driver of disease progression. Chronic inflammation in MPNs is characterized by persistent connective tissue remodeling, which leads to organ dysfunction and ultimately, organ failure, due to excessive accumulation of extracellular matrix (ECM). Considering that MPNs are acquired clonal stem cell diseases developing in an inflammatory microenvironment in which the hematopoietic cell populations are progressively replaced by stromal proliferation-"a wound that never heals"-we herein aim to provide a comprehensive review of previous promising research in the field of circulating ECM fragments in the diagnosis, treatment and monitoring of MPNs. We address the rationales and highlight new perspectives for the use of circulating ECM protein fragments as biologically plausible, noninvasive disease markers in the management of MPNs.

Cytological Diagnosis of Classic Myeloproliferative Neoplasms at the Age of Molecular Biology

Myeloproliferative neoplasms (MPN) are clonal hematopoietic stem cell-derived disorders characterized by uncontrolled proliferation of differentiated myeloid cells. Two main groups of MPN, BCR::ABL1-positive (Chronic Myeloid Leukemia) and BCR::ABL1-negative (Polycythemia Vera, Essential Thrombocytosis, Primary Myelofibrosis) are distinguished. For many years, cytomorphologic and histologic features were the only proof of MPN and attempted to distinguish the different entities of the subgroup BCR::ABL1-negative MPN. World Health Organization (WHO) classification of myeloid neoplasms evolves over the years and increasingly considers molecular abnormalities to prove the clonal hematopoiesis. In addition to morphological clues, the detection of JAK2, MPL and CALR mutations are considered driver events belonging to the major diagnostic criteria of BCR::ABL1-negative MPN. This highlights the preponderant place of molecular features in the MPN diagnosis. Moreover, the advent of next-generation sequencing (NGS) allowed the identification of additional somatic mutations involved in clonal hematopoiesis and playing a role in the prognosis of MPN. Nowadays, careful cytomorphology and molecular biology are inseparable and complementary to provide a specific diagnosis and to permit the best follow-up of these diseases.

Applications of Artificial Intelligence in Philadelphia-Negative Myeloproliferative Neoplasms

Philadelphia-negative (Ph-) myeloproliferative neoplasms (MPNs) are a group of hematopoietic malignancies identified by clonal proliferation of blood cell lineages and encompasses polycythemia vera (PV), essential thrombocythemia (ET), and primary myelofibrosis (PMF). The clinical and laboratory features of Philadelphia-negative MPNs are similar, making them difficult to diagnose, especially in the preliminary stages. Because treatment goals and progression risk differ amongst MPNs, accurate classification and prognostication are critical for optimal management. Artificial intelligence (AI) and machine learning (ML) algorithms provide a plethora of possible tools to clinicians in general, and particularly in the field of malignant hematology, to better improve diagnosis, prognosis, therapy planning, and fundamental knowledge. In this review, we summarize the literature discussing the application of AI and ML algorithms in patients with diagnosed or suspected Philadelphia-negative MPNs. A literature search was conducted on PubMed/MEDLINE, Embase, Scopus, and Web of Science databases and yielded 125 studies, out of which 17 studies were included after screening. The included studies demonstrated the potential for the practical use of ML and AI in the diagnosis, prognosis, and genomic landscaping of patients with Philadelphia-negative MPNs.

Myeloproliferative neoplasms and splanchnic vein thrombosis: Contemporary diagnostic and therapeutic strategies

Myeloproliferative neoplasms (MPNs) are the most common etiologies of primary splanchnic vein thrombosis, present in almost forty percent of patients with Budd-Chiari syndrome or portal vein thrombosis. Diagnosis of MPNs can be difficult in these patients because key characteristics, such as elevated blood cell counts and splenomegaly, are confounded by portal hypertension or bleeding complications. In recent years, diagnostic tools have improved to provide more accurate diagnosis and classification of MPNs. Although bone marrow biopsy findings remain a major diagnostic criterion, molecular markers are playing an increasing role not only in diagnosis but also in better estimating prognosis. Therefore, though screening for JAK2V617F mutation should be the starting point of the diagnostic workup performed in all patients with splanchnic vein thrombosis, a multidisciplinary approach is needed to accurately diagnose the subtype of myeloproliferative neoplasm, recommend the useful additional tests (bone marrow biopsy, search for an additional mutation using targeted next-generation sequencing), and suggest the best treatment strategy. Indeed, providing a specific expert care pathway for patients with splanchnic vein thrombosis and underlying myeloproliferative neoplasm is crucial to determine the optimal management to reduce the risk of both hematological and hepatic complications.

Myelofibrosis-type megakaryocyte dysplasia (MTMD) as a distinct category of BCR::ABL-negative myeloproliferative neoplasms. Challenges and perspectives

In this Perspective, we discuss criteria for defining a new disease entity or variant of a recognized disease or disorder. We do so in the context of the current topography of the BCR::ABL-negative myeloproliferative neoplasms (MPNs) where two new variants are reported: clonal megakaryocyte dysplasia with normal blood values (CMD-NBV) and clonal megakaryocyte dysplasia with isolated thrombocytosis (CMD-IT). The cardinal feature of these variants is bone marrow megakaryocyte hyperplasia and atypia corresponding the WHO histological criteria for primary myelofibrosis (myelofibrosis-type megakaryocyte dysplasia-MTMD). Persons with these new variants have a different disease course and features from others in the MPN domain. In a broader context we suggest myelofibrosis-type megakaryocyte dysplasia defines a spectrum of related MPN variants including CMD-NBV, CMD-IT, pre-fibrotic myelofibrosis and overt myelofibrosis, which differ from polycythemia vera and essential thrombocythemia. Our proposal needs external validation and we stress the need for a consensus definition of the megakaryocyte dysplasia which is the hallmark of these disorders.

Artificial Intelligence in Bone Marrow Histological Diagnostics: Potential Applications and Challenges

The expanding digitalization of routine diagnostic histological slides holds a potential to apply artificial intelligence (AI) to pathology, including bone marrow (BM) histology. In this perspective, we describe potential tasks in diagnostics that can be supported, investigations that can be guided, and questions that can be answered by the future application of AI on whole-slide images of BM biopsies. These range from characterization of cell lineages and quantification of cells and stromal structures to disease prediction. First glimpses show an exciting potential to detect subtle phenotypic changes with AI that are due to specific genotypes. The discussion is illustrated by examples of current AI research using BM biopsy slides. In addition, we briefly discuss current challenges for implementation of AI-supported diagnostics.